The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Improved performance of real time system Health Management (HM) and maintenance functions is becoming a primary design goal of complex systems. Legacy approaches to system design, which focused primarily on minimizing initial acquisition costs, have been generally inadequate to address availability-driven design (i.e., design approaches focused on the relative availability of the system for use based on its operational health). While tools that support availability modeling are not new, traditional acquisition processes have not generally emphasized their importance, and attempts to include availability as a primary design metric have often met with resistance.
Similarly, although the application of Integrated Diagnostics (ID) processes has been advocated in some contractor circles (e.g., U.S. Department of Defense) for nearly two decades, such efforts to guide the integration and verification of system diagnostic requirements across levels of indenture and maintenance have also failed to become core elements of system development.
Finally, even when a legacy program has been given a mandate for supportability improvement, traditional organizations, tools and processes have often been set up for less than optimal performance. Internal engineering silos, conflicting organizational interests, a lack of reusable technologies and processes that tend to be disjointed over the product life cycle have all contributed to inefficiencies that have increased development costs and thus further hampered supportability efforts. For example, an accurate understanding of failure propagation is required for many traditional reliability, testability, maintainability, and safety engineering analyses, at both contractor and supplier sites. Historically, each discipline has performed its own independent analysis of system health characteristics, using independent tools, processes and engineering assumptions. This has proven to be neither consistent nor affordable.
All of these historical shortcomings are exacerbated by increasing system complexity. Historically, HM capabilities have been developed in an ad hoc manner, and have not performed up to the expectations of those responsible for the system design/implementation. Therefore, disciplined approaches are required which leverage and integrate traditional engineering analyses in support of a coherent and traceable HM development process that strives to maximize metrics typically utilized as success criteria for system health and life cycle support issues. An integrated model-driven approach provides a structured means for potentially achieving such improvements.
A common way to represent system behavior is to create a model of that behavior. Modeling approaches vary considerably—their usefulness is proportional to the degree to which they can represent interesting aspects of system behavior in a way that reduces or abstracts the complexities of collateral behavior. There are many categories of models; mental, physical, verbal, etc. In engineering applications, both qualitative (descriptive) and quantitative (mathematical) models, which may take various forms (e.g., text, spreadsheets, graphical representations, static or dynamic networks, etc), are often employed. In general, models support analysis of important aspects of system behavior and enable dynamic views of that behavior via calculation, visualization, simulation, etc. Correctly designed models allow engineers and analysts to draw conclusions and predictions of system behavior in ways otherwise unattainable.
The true power of model-based approaches to system design and analysis would be realized if synergies between system models could be discovered and realized. Such discoveries would enable the outcome of one analysis to serve as the basis or input for another. With such an approach, significant gains in the overall accuracy, efficiency and traceability of analytical design activities could be realized. Of course, a proliferation of models can introduce issues of complexity, data redundancy and management, and the like. Techniques for model integration and reuse of supporting engineering data would be required in order to maximize the utility and benefits to be gained from a model-driven approach to system design.